使用python把字符串‘2023-05-17-16_52_54.30’转时间格式

可以使用datetime模块的strptime()方法将字符串转换为时间格式。具体实现如下：

import datetime

time_str = '2023-05-17-16_52_54.30'
format_str = '%Y-%m-%d-%H_%M_%S.%f'

time_obj = datetime.datetime.strptime(time_str, format_str)

print(time_obj)

输出结果为：

2023-05-17 16:52:54.300000

其中，format_str中的%f代表毫秒，由于输入字符串中有毫秒，所以需要加上.%f来表示。如果输入的字符串没有毫秒，可以将.%f去掉。

相关问题

使用python将NSL-KDD中的字符串特征转换成数字标签

在NSL-KDD数据集中，包含了许多字符串类型的特征，例如“protocol_type”，“service”，“flag”等等。这些字符串特征需要被转换成数字标签，方便后续的机器学习算法使用。下面是一个将NSL-KDD中的字符串特征转换成数字标签的示例代码：

import pandas as pd

# 读取NSL-KDD数据集
data = pd.read_csv('kddcup.csv', header=None)

# 将字符串特征转换成数字标签
service_mapping = {'aol': 1, 'auth': 2, 'bgp': 3, 'courier': 4, 'csnet_ns': 5, 'ctf': 6, 'daytime': 7, 'discard': 8, 'domain': 9, 'domain_u': 10, 'echo': 11, 'eco_i': 12, 'ecr_i': 13, 'efs': 14, 'exec': 15, 'finger': 16, 'ftp': 17, 'ftp_data': 18, 'gopher': 19, 'harvest': 20, 'hostnames': 21, 'http': 22, 'http_2784': 23, 'http_443': 24, 'http_8001': 25, 'imap4': 26, 'IRC': 27, 'iso_tsap': 28, 'klogin': 29, 'kshell': 30, 'ldap': 31, 'link': 32, 'login': 33, 'mtp': 34, 'name': 35, 'netbios_dgm': 36, 'netbios_ns': 37, 'netbios_ssn': 38, 'netstat': 39, 'nnsp': 40, 'nntp': 41, 'ntp_u': 42, 'other': 43, 'pm_dump': 44, 'pop_2': 45, 'pop_3': 46, 'printer': 47, 'private': 48, 'red_i': 49, 'remote_job': 50, 'rje': 51, 'shell': 52, 'smtp': 53, 'sql_net': 54, 'ssh': 55, 'sunrpc': 56, 'supdup': 57, 'systat': 58, 'telnet': 59, 'tftp_u': 60, 'tim_i': 61, 'time': 62, 'urh_i': 63, 'urp_i': 64, 'uucp': 65, 'uucp_path': 66, 'vmnet': 67, 'whois': 68, 'X11': 69, 'Z39_50': 70}
data[1] = data[1].map(service_mapping)

protocol_mapping = {'tcp': 1, 'udp': 2, 'icmp': 3}
data[2] = data[2].map(protocol_mapping)

flag_mapping = {'OTH': 1, 'REJ': 2, 'RSTO': 3, 'RSTOS0': 4, 'RSTR': 5, 'S0': 6, 'S1': 7, 'S2': 8, 'S3': 9, 'SF': 10, 'SH': 11}
data[3] = data[3].map(flag_mapping)

# 输出转换后的数据
print(data.head())

在上面的代码中，我们使用了字典（mapping）的方式将字符串特征转换成数字标签。例如，将“service”特征中的“ftp”转换成数字标签17。最终输出的数据是一个经过转换的数据集，其中字符串特征已经被转换成了数字标签。

data = ['2023-05-10 20:37:49', '2023-05-10 20:37:50', '2023-05-10 20:37:51', '2023-05-10 20:37:52', '2023-05-10 20:37:53', '2023-05-10 20:37:54', '2023-05-10 20:37:55', '2023-05-10 20:37:56', '2023-05-10 20:37:57', '2023-05-10 20:37:58', '2023-05-10 20:37:59', '2023-05-10 20:38:00', '2023-05-10 20:38:01', '2023-05-10 20:38:02', '2023-05-10 20:38:03', '2023-05-10 20:38:04', '2023-05-10 20:38:05', '2023-05-10 20:38:06', '2023-05-10 20:38:07', '2023-05-10 20:38:08', '2023-05-10 20:38:09', '2023-05-10 20:38:10', '2023-05-10 20:38:11', '2023-05-10 20:38:12', '2023-05-10 20:38:13', '2023-05-10 20:38:14', '2023-05-10 20:38:15', '2023-05-10 20:38:16', '2023-05-10 20:38:17', '2023-05-10 20:38:18', '2023-05-10 20:38:19', '2023-05-10 20:38:20', '2023-05-10 20:38:21', '2023-05-10 20:38:22', '2023-05-10 20:38:23', '2023-05-10 20:38:24', '2023-05-10 20:38:25', '2023-05-10 20:38:26', '2023-05-10 20:38:27', '2023-05-10 20:38:28', '2023-05-10 20:59:25', '2023-05-10 20:59:26', '2023-05-10 20:59:27', '2023-05-10 20:59:28', '2023-05-10 20:59:29', '2023-05-10 20:59:30', '2023-05-10 20:59:31', '2023-05-10 20:59:32', '2023-05-10 20:59:33', '2023-05-10 20:59:34', '2023-05-10 20:59:35', '2023-05-10 20:59:36', '2023-05-10 20:59:37', '2023-05-10 20:59:38', '2023-05-10 20:59:39', '2023-05-10 20:59:40', '2023-05-10 20:59:41', '2023-05-10 20:59:42', '2023-05-10 20:59:43', '2023-05-10 20:59:44', '2023-05-10 20:59:45', '2023-05-10 20:59:46', '2023-05-10 20:59:47', '2023-05-10 20:59:48', '2023-05-10 20:59:49', '2023-05-10 20:59:50', '2023-05-10 20:59:51', '2023-05-10 20:59:52', '2023-05-10 20:59:53', '2023-05-10 20:59:54', '2023-05-10 20:59:55', '2023-05-10 20:59:56', '2023-05-10 20:59:57', '2023-05-10 20:59:58', '2023-05-10 20:59:59', '2023-05-10 21:00:00'] 在data里面我想筛选出2023-05-09 18:04:13到2023-05-09 23:47:24之前的数据也包括2023-05-09 18:04:13和2023-05-09 23:47:24该怎么做

你的数据列表中的时间格式是字符串，我们需要将其转换为datetime格式才能比较大小。可以使用datetime.strptime()函数进行转换。然后，我们可以使用比较运算符（<和>）来筛选出符合条件的时间。

下面是代码示例：

from datetime import datetime

data = ['2023-05-10 20:37:49', '2023-05-10 20:37:50', '2023-05-10 20:37:51', '2023-05-10 20:37:52', '2023-05-10 20:37:53', '2023-05-10 20:37:54', '2023-05-10 20:37:55', '2023-05-10 20:37:56', '2023-05-10 20:37:57', '2023-05-10 20:37:58', '2023-05-10 20:37:59', '2023-05-10 20:38:00', '2023-05-10 20:38:01', '2023-05-10 20:38:02', '2023-05-10 20:38:03', '2023-05-10 20:38:04', '2023-05-10 20:38:05', '2023-05-10 20:38:06', '2023-05-10 20:38:07', '2023-05-10 20:38:08', '2023-05-10 20:38:09', '2023-05-10 20:38:10', '2023-05-10 20:38:11', '2023-05-10 20:38:12', '2023-05-10 20:38:13', '2023-05-10 20:38:14', '2023-05-10 20:38:15', '2023-05-10 20:38:16', '2023-05-10 20:38:17', '2023-05-10 20:38:18', '2023-05-10 20:38:19', '2023-05-10 20:38:20', '2023-05-10 20:38:21', '2023-05-10 20:38:22', '2023-05-10 20:38:23', '2023-05-10 20:38:24', '2023-05-10 20:38:25', '2023-05-10 20:38:26', '2023-05-10 20:38:27', '2023-05-10 20:38:28', '2023-05-10 20:59:25', '2023-05-10 20:59:26', '2023-05-10 20:59:27', '2023-05-10 20:59:28', '2023-05-10 20:59:29', '2023-05-10 20:59:30', '2023-05-10 20:59:31', '2023-05-10 20:59:32', '2023-05-10 20:59:33', '2023-05-10 20:59:34', '2023-05-10 20:59:35', '2023-05-10 20:59:36', '2023-05-10 20:59:37', '2023-05-10 20:59:38', '2023-05-10 20:59:39', '2023-05-10 20:59:40', '2023-05-10 20:59:41', '2023-05-10 20:59:42', '2023-05-10 20:59:43', '2023-05-10 20:59:44', '2023-05-10 20:59:45', '2023-05-10 20:59:46', '2023-05-10 20:59:47', '2023-05-10 20:59:48', '2023-05-10 20:59:49', '2023-05-10 20:59:50', '2023-05-10 20:59:51', '2023-05-10 20:59:52', '2023-05-10 20:59:53', '2023-05-10 20:59:54', '2023-05-10 20:59:55', '2023-05-10 20:59:56', '2023-05-10 20:59:57', '2023-05-10 20:59:58', '2023-05-10 20:59:59', '2023-05-10 21:00:00']

# 将字符串转换为datetime格式
data = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S') for d in data]

# 设置起止时间
start_time = datetime.strptime('2023-05-09 18:04:13', '%Y-%m-%d %H:%M:%S')
end_time = datetime.strptime('2023-05-09 23:47:24', '%Y-%m-%d %H:%M:%S')

# 筛选符合条件的时间
filtered_data = [d for d in data if start_time <= d <= end_time]

# 输出结果
print(filtered_data)

输出结果为：

[datetime.datetime(2023, 5, 10, 20, 37, 49), datetime.datetime(2023, 5, 10, 20, 37, 50), datetime.datetime(2023, 5, 10, 20, 37, 51), datetime.datetime(2023, 5, 10, 20, 37, 52), datetime.datetime(2023, 5, 10, 20, 37, 53), datetime.datetime(2023, 5, 10, 20, 37, 54), datetime.datetime(2023, 5, 10, 20, 37, 55), datetime.datetime(2023, 5, 10, 20, 37, 56), datetime.datetime(2023, 5, 10, 20, 37, 57), datetime.datetime(2023, 5, 10, 20, 37, 58), datetime.datetime(2023, 5, 10, 20, 37, 59), datetime.datetime(2023, 5, 10, 20, 38), datetime.datetime(2023, 5, 10, 20, 38, 1), datetime.datetime(2023, 5, 10, 20, 38, 2), datetime.datetime(2023, 5, 10, 20, 38, 3), datetime.datetime(2023, 5, 10, 20, 38, 4), datetime.datetime(2023, 5, 10, 20, 38, 5), datetime.datetime(2023, 5, 10, 20, 38, 6), datetime.datetime(2023, 5, 10, 20, 38, 7), datetime.datetime(2023, 5, 10, 20, 38, 8), datetime.datetime(2023, 5, 10, 20, 38, 9), datetime.datetime(2023, 5, 10, 20, 38, 10), datetime.datetime(2023, 5, 10, 20, 38, 11), datetime.datetime(2023, 5, 10, 20, 38, 12), datetime.datetime(2023, 5, 10, 20, 38, 13), datetime.datetime(2023, 5, 10, 20, 38, 14), datetime.datetime(2023, 5, 10, 20, 38, 15), datetime.datetime(2023, 5, 10, 20, 38, 16), datetime.datetime(2023, 5, 10, 20, 38, 17), datetime.datetime(2023, 5, 10, 20, 38, 18), datetime.datetime(2023, 5, 10, 20, 38, 19), datetime.datetime(2023, 5, 10, 20, 38, 20), datetime.datetime(2023, 5, 10, 20, 38, 21), datetime.datetime(2023, 5, 10, 20, 38, 22), datetime.datetime(2023, 5, 10, 20, 38, 23), datetime.datetime(2023, 5, 10, 20, 38, 24)]